Challenges in Computational & Functional GenomicsIgor Ulitsky

“the branch of genetics that studies organisms in terms of their genomes (their full DNA sequences)”

Computational genomics in TAU◦ Ron Shamir’s lab – focus on gene expression and

regulatory networks◦ Eithan Ruppin’s lab – focus on metabolism◦ Tal Pupko’s and Benny Chor’s labs – focus on

phylogeny◦ Roded Sharan’s lab – focus on networks◦ Noam Shomron’s lab – focus on miRNA◦ Eran Halperin’s lab – focus on genetics

Genomics

Alignment Protein coding gene finding Assembly of long reads Basic microarray data analysis Mapping of transcriptional regulation in

simple organisms Functional profiling in simple organisms

“Solved” problems

Determining protein abundance Assembly of short reads Transcriptional regulation in higher

eukaryotes “Histone code”: Chromatin modifications,

their function and regulation Functional profiling of mammalian cells Association studies for single-gene effects Construction and modeling of synthetic

circuits

“Worked on” problems

Digital gene expression from RNA-seq studies

Prediction of ncRNAs and their function Global mapping of alternative splicing

regulation Integration of multi-level signaling (TFs,

miRNA, chromatin) Association studies for combinations of

alleles

“Future” problems

All microbial genomes are sequenced in E. coli Each sequencing efforts basically introduces

genes (3-8Kb fragments) into E. coli Sometimes sequencing fails Idea: sequencing fails barrier to horizontal gene

transfer

Using sequencing to find new antibiotics

Even sequencing of reads with 100s of bp will no identify many indels

Idea: sequence pairs of sequences at some distance apart from each other

Using sequencing to uncover structural variation

High-throughput sequencing can identify all the mutations in different cancers

20,857 transcripts from 18,191 human genes sequenced in 11 breast and 11 colorectal cancers.

Mutational landscape of human cancer

Problems: few mutations are drivers most are passangers

Most studies did not identify high frequent risk allels

But: members of some pathways are affected in almost any tumour

Network biology needed

Mutational landscape of human cancer

Predicting ncRNAs Using histone

modifications and sequence conservation to uncover long non-coding RNAs (lincRNA)

12 fly species were sequenced to identify ◦ Evolution of genes and chromosome◦ Evolutionary constrained sequence elements in

promoters and 3’ UTRs Starting point – genome-wide alignment of

the genomes

Using conservation to uncover regulatory elements